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   "execution_count": 8,
   "id": "192fd481-9dc9-459b-8ecc-42da693c1f2d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "train_data = pd.read_csv(\"糖尿病风险预测挑战赛公开数据/train.csv\")\n",
    "test_data = pd.read_csv(\"糖尿病风险预测挑战赛公开数据/test.csv\")"
   ]
  },
  {
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   "execution_count": 9,
   "id": "77fc8169-7d38-407d-9770-604d4e94eecb",
   "metadata": {
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   },
   "outputs": [
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       "      <th>Smoker</th>\n",
       "      <th>Stroke</th>\n",
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       "      <th>Fruits</th>\n",
       "      <th>...</th>\n",
       "      <th>NoDocbcCost</th>\n",
       "      <th>GenHlth</th>\n",
       "      <th>MentHlth</th>\n",
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       "      <th>DiffWalk</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>Income</th>\n",
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       "   id  HighBP  HighChol  CholCheck       BMI  Smoker  Stroke  \\\n",
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       "1   1     0.0       1.0        1.0  28.00000     1.0     0.0   \n",
       "\n",
       "   HeartDiseaseorAttack  PhysActivity  Fruits  ...  NoDocbcCost  GenHlth  \\\n",
       "0                   0.0           1.0     1.0  ...          0.0      2.0   \n",
       "1                   0.0           1.0     1.0  ...          0.0      2.0   \n",
       "\n",
       "   MentHlth  PhysHlth  DiffWalk  Sex  Age  Education  Income  target  \n",
       "0   1.88054       0.0       0.0  0.0  6.0        6.0     8.0     0.0  \n",
       "1   2.00000       0.0       0.0  0.0  7.0        6.0     8.0     0.0  \n",
       "\n",
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     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2d83ae18-6aea-42cf-afe6-7089915f3a7e",
   "metadata": {
    "tags": []
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   "outputs": [
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       "       id  HighBP  HighChol  CholCheck        BMI  Smoker  Stroke  \\\n",
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       "1  300001     1.0       1.0        1.0  27.285374     1.0     0.0   \n",
       "\n",
       "   HeartDiseaseorAttack  PhysActivity    Fruits  ...  AnyHealthcare  \\\n",
       "0                   0.0           1.0  1.000000  ...            1.0   \n",
       "1                   0.0           1.0  0.714626  ...            1.0   \n",
       "\n",
       "   NoDocbcCost   GenHlth  MentHlth   PhysHlth  DiffWalk  Sex        Age  \\\n",
       "0          0.0  2.000000      15.0  30.000000       0.0  0.0   9.000000   \n",
       "1          0.0  4.714626       0.0  10.570748       1.0  0.0  12.285374   \n",
       "\n",
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   "id": "fc133b29-9f89-4a7d-966d-fcba8d2aa986",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                        int64\n",
       "HighBP                  float64\n",
       "HighChol                float64\n",
       "CholCheck               float64\n",
       "BMI                     float64\n",
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       "Stroke                  float64\n",
       "HeartDiseaseorAttack    float64\n",
       "PhysActivity            float64\n",
       "Fruits                  float64\n",
       "Veggies                 float64\n",
       "HvyAlcoholConsump       float64\n",
       "AnyHealthcare           float64\n",
       "NoDocbcCost             float64\n",
       "GenHlth                 float64\n",
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       "PhysHlth                float64\n",
       "DiffWalk                float64\n",
       "Sex                     float64\n",
       "Age                     float64\n",
       "Education               float64\n",
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      ]
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    "train_data.dtypes"
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   "id": "6ad58238-8829-4923-af9c-1fef33ab24c0",
   "metadata": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
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     },
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     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train_data.iloc[:, 1:].corr().round(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8bafb944-50fb-48a5-8db1-c8444f2be878",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data.iloc[:, 1:].corr().target.iloc[:-1].plot(kind='barh', figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "073afba5-2128-4ceb-ab1a-358ec2cbfc27",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.336207 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 5352\n",
      "[LightGBM] [Info] Number of data points in the train set: 300000, number of used features: 21\n",
      "[LightGBM] [Info] Start training from score -0.174353\n",
      "[LightGBM] [Info] Start training from score -3.912023\n",
      "[LightGBM] [Info] Start training from score -1.966113\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LGBMClassifier()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "m = LGBMClassifier()\n",
    "m.fit(\n",
    "    train_data.drop([\"id\", \"target\"], axis=1),\n",
    "    train_data[\"target\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8bd3d796-fc16-4e3f-a60b-2ee5ff6a22bd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pd.DataFrame({\n",
    "    'id': test_data['id'],\n",
    "    'target': m.predict(test_data.drop([\"id\"], axis=1))\n",
    "}).to_csv(\"submit.csv\", index=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc000136-6522-487b-8960-832d5fd18f9d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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